基于图像梯度信息强化的SIFT特征匹配算法改进

Improvement of SIFT Feature Matching Algorithm Based on Image Gradient Information Enhancement

针对传统特征匹配算法匹配率低的问题,提出一种基于图像梯度信息强化的尺度不变特征转换(SIFT)特征匹配算法的改进算法.首先通过适当的梯度算子求出梯度图;然后以特定权值将梯度图与原图融合,归一化后对融合图像进行高斯模糊;最后利用传统算法进行特征提取.实验结果表明,改进算法的视角、旋转不变性明显优于原算法,对亮度变化较大或有噪声的图像匹配率也略有提升,有效提高了SIFT特征匹配算法的准确性.

Aiming at the problem of low matching rate of traditional feature matching algorithms, we proposed an improved algorithm based on enhanced image gradient information for scale-invariant feature transform （SIFT） feature matching algorithm. Firstly, a gradient image was obtained by proper gradient operator. Secondly, the gradient image and the original image were fused with the specific weight, and after normalization, the fused image was blurred by Gauss. Finally, the traditional algorithm was used for feature extraction. Experimental results show that the visual angle and invariability of rotation of the improved algorithm are obviously better than those of the original algorithm, and the matching rate of the images with larger brightness or noise is also slightly improved, which effectively improves the accuracy of the SIFT feature matching algorithm.